WPS3921
Does access to credit improve productivity?
Evidence from Bulgarian firms
Roberta Gatti
Development Research Group
The World Bank
and CEPR
Inessa Love
Development Research Group
The World Bank
Abstract
Although it is widely accepted that financial development is associated with
higher growth, the evidence on the channels through which credit affects growth
on the micro-level is scant. Using data from a cross section of Bulgarian firms,
we estimate the impact of access to credit (as proxied by indicators of whether
firms have access to a credit or overdraft facility) on productivity. To overcome
potential omitted variable bias of OLS estimates, we use information on firms'
past growth to instrument for access to credit. We find credit to be positively
and strongly associated with TFP. These results are robust to a wide range of
robustness checks.
World Bank Policy Research Working Paper 3921, May 2006
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage
the exchange of ideas about development issues. An objective of the series is to get the findings out
quickly, even if the presentations are less than fully polished. The papers carry the names of the
authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in
this paper are entirely those of the authors. They do not necessarily represent the view of the World
Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are
available online at http://econ.worldbank.org.
We thank Mary Hallward-Driemeier for useful discussions. Address correspondence to
rgatti@worldbank.org.
1. Introduction
The link between finance and growth has become one of the stylized facts in the
recent development literature.. Recent evidence from transition economies supports
this widely accepted view (see Koivu (2002), Coricelli and Masten (2004) and Beck
and Laeven, (2004)). However, the precise channels through which finance operates
are still unclear. For example, Levine (2005) suggests that finance may influence
long-run growth through its impact on savings rates, investment decisions and
technological innovation. However, in the same article Levine states that "We are far
from definite answers to these questions: Does finance cause growth, and if it does,
how?". In this paper we provide evidence to answer the second question.
One possible channel through which finance affects growth is via improvement in
productivity. Several models provide theoretical justification to the proposition that
credit affects growth through its impact on productivity. In these models financial
sector provides real services through alleviation of information and transaction costs,
in particular making the longer-gestation higher return projects more attractive (see,
for example, Levine (1991) and Bencivenga, Smith and Starr (1995)). However, the
existing empirical evidence on this channel is still limited. At the macro level,
Easterly and Levine (2001) show that total factor productivity (TFP) accounts for
most of the variation in the cross-country differences in economic development and
growth. They go as far as to claim that factor accumulation is not important for future
growth and but productivity is.1 Levine and Zervos (1998) argue that "the major
channel through which growth is linked to stock markets and banks is through
1A recent paper by Bond et al. (2004) questions that view and shows that capital accumulation is
important for growth.
2
productivity growth." However, rapid credit growth might also have a reverse,
negative, impact on productivity. For example, Ghani and Suri (1999) argue that the
rapid growth of bank credit was associated with negative productivity growth in
Malaysia because allocation of credit was inefficient.
We use information from a cross section of Bulgarian firms to estimate whether
access to credit is associated, and causally so, with higher productivity. To our
knowledge, there are only a few firm-level studies that attempt to evaluate the effect
of access to credit on total factor productivity. Bernstein and Nadiri (1993) estimate
the effect of financial structure on productivity growth in US manufacturing
companies. Their focus is on estimating the impact of agency cost of debt and the
signaling benefits of dividends on productivity growth. Nickell and Nicholitsas (1999)
find that financial pressure (defined as the ratio of interest payments to cash flow) has
a positive effect on productivity. They deal with endogeneity of financial pressure by
using instruments of lagged debt burden and yield on treasury bonds. Using data from
the UK and Italy, Schiantarelli and Sembenelli (1999) show that firms with a larger
proportion of long-term debt in their capital structure have improved subsequent
performance measured as profitability, sales growth and total factor productivity.
Similar patterns are found in Schiantarelli and Jaramillo (1999) for Ecuador and
Schiantarelli and Srivastava (1999) for India. However, due to data limitations, all the
previous studies focused on the effect of leverage on productivity.2 In our paper we
are able to use more direct measures of access to credit, such as presence or absence
of overdrafts and lines of credit. Several recent papers estimated TFP in transitional
economies. Hoekman and Djankov (1999) estimate the effects of trade liberalization
2A related strand of literature focused on the relationship between productivity and leverage in firms
that have undergone a leveraged buyout. (See Lichtenberg and Siegel (1990), Ravencraft and Scherer
(1987)).
3
and access to global markets on TFP growth in Bulgaria. They argue that firms that
increase their imports of intermediate and capital goods have higher productivity
growth.3 Access to capital to finance these imports could be an important driving
force for these productivity improvements, which is the focus of our paper. A related
paper by Maurel (2001) estimated TFP for a panel of Hungarian firms but is focused
mainly on the effect of investment on TFP and not on the relationship between access
to credit and TFP, which we investigate here.
Our paper contributes to the existing literature in a number of ways. First, we are able
to test directly for the importance of credit for productivity by using indicators of
access to credit (whether a firm has access to a credit line or overdraft). As OLS
estimates of access to credit on productivity potentially suffer from omitted variable
bias, we use past information about firm growth to instrument for access to credit in
order to obtain two stage estimates and identify a causal impact of credit on
productivity. We find that, indeed, access to credit has a casual positive effect on
productivity. We then subject our results to a number of robustness checks to verify
that indeed our instruments are valid in this context.
Assessing the role of credit in determining productivity is also particularly relevant
from a policy point of view in the context of Bulgaria. In the late 1900s irresponsible
quasi-fiscal policies brought about a deep financial crisis in Bulgaria (1996-97) that
resulted in hyperinflation reaching peaks of 1000% and in a dramatic drop in private
investment. Following the crisis, the government adopted a strong commitment to
fiscal responsibility by introducing a currency board and a broad range of market
3Interestingly, Hoekman and Djankov (1999) find that the percent of output exported has no significant
effect on total factor productivity.
4
oriented reforms. Because of the collapse of the financial sector, virtually no credit
was available to the private sector. However, starting in 2001, credit to the private
sector grew at progressively faster rates. For example, bank claims on the non-
government sector rose by nearly 8 percentage points of GDP in 2003 (IMF, Article
IV consultations) without any sign of deterioration in banks' prudential indicators.
Such rapid credit growth and the related widening of the current account deficit
generated concerns of overheating in the economy and induced the authorities to
implement restrictive measures in October 2004 to curb it.
The paper is organized as follows. Section 2 described the data. Section 3 discusses
alternative TFP estimates and methodology. Section 4 presents the results of the two-
stage estimation and the robustness checks. Section 5 concludes.
2. Data description
We use a recent survey of Bulgarian enterprises which was conducted by the
IFC/World Bank in March-April 2004 (FIAS ). The survey contains information on
548 Bulgarian firms sampled according to a number of criteria: (i) size, so as to be
representative of SMEs and to include a minimum of 20% of large firms; (ii) sectors,
so as to mirror the distributions of Bulgarian firms across manufacturing, mining, and
services; and (iii) location, so as to include firms in large cities (200), small towns
(100), and the capital, Sofia. The survey reports detailed information on
administrative and bureaucratic constraints to business and a limited amount of
5
balance sheet-type data.4 Table 1, Panel A reports distribution of firms in our sample
by industry and size.
About 60% of the surveyed firms work in manufacturing and 30% is engaged in
service activities. Access to selling markets is fairly dichotomous: 63% of the firms
sell only domestically ­ these are mostly micro and small enterprises engaged in
manufacturing and services.5 Exporters sell on average more than 60% of their output
to foreigners, indicating that there might be important costs to set up production for
export. About 75% of the exporters sell to EU markets (to Germany, Italy, and
Greece). Half of these sell also to Eastern Europe and Central Asia markets, in
particular to Macedonia, Russia, and Turkey.
Foreign ownership is highly concentrated. In the sample for which TFP can be
estimated, 10% of firms are foreign owned and, among these, 75% of firm capital is in
foreign hands.
Firms report, amongst others, the value of total sales and fixed assets as well as
information on employees, wages and costs as a percentage of total sales. We use this
information to obtain estimates of TFP.
The survey has several different indicators of access to credit. Firms report whether
they have a credit line or an overdraft facility. As our main indicator of access to
credit, we use a variable (LINE) taking value of one if the firm has either overdraft or
a credit line and zero otherwise. We combine overdrafts and credit lines together as
4See "Investment Climate and Regulatory Cost Survey," IFC, for more details on the survey.
5Firm size is defined as follows: micro enterprises (up to 10 employees); small (between 11 and 50
employees); medium (between 51 and 100 employees); large (more than 100 employees).
6
both instruments represent easy access to immediate liquidity and both have short-
term maturity. About 18% of firms have an overdraft facility and about 20% have a
line of credit, and about 30% of firms in our sample have either overdraft or credit
line. Credit availability increases with firm size: only 10% of micro enterprises,
between 20% and 30% of small and medium enterprises, and about 40% of large
enterprises have a credit line or overdraft. Table 1, Panel B reports distribution of
firms with and without access to credit, by size.
The survey also asks firms to rank a number of different obstacles to doing business
(rankings range from no obstacle to major obstacle). Access to credit is listed as one
of the major obstacles. Interestingly, there is no correlation between the extent to
which firms rank access to credit to be an obstacle to business and firm size. While
access to a credit line or availability of debt are objective measures of access to credit,
the obstacle rankings are very subjective, and are likely to depend on personal
characteristics of managers. In fact, there is no significant correlation between
obstacles rankings and actual presence of credit lines and debt. Therefore, we prefer
to use objective measures of access, such as our variable LINE.
Table 2 reports descriptive statistics for the main variables.
3. Estimating productivity
Firm productivity is an unobservable firm characteristics. However, estimates of
productivity can be recovered as the difference between actual output and output
7
estimated by a production function using actual input quantities. Productivity
estimates can be obtained from a regression of the type:
lnYi = + k ln Ki + l ln Li + i (1)
where Yi is firm's output, K and L are capital and labor, k and l are capital and
labor shares and i is the error. In this model, TFP, the estimated residual, is obtained
as the difference between actual and predicted output, or ^i = lnYi - lnY^i .
The simplest model can be estimated by OLS. However, econometric issues arise
because firm productivity can affect input choices. For example, firms that receive a
productivity shock may alter their mix of inputs. This implies that the error and the
regressors in (1) might be correlated and that coefficient estimates obtained with OLS
might be biased. A number of solutions have been proposed in the literature to
overcome this problem. These include using firm-level fixed effects, that would deal
with time-invariant individual effects, and instrumental variable strategy for input
choices. Following Olley and Pakes (1996), Levinsohn and Petrin (2000) argued that
using information on intermediate input choices such as demand for electricity ­
which tracks productivity shocks quite closely and cannot be stored ­ one can
effectively control for productivity shocks and thus obtain consistent and unbiased
estimates of k and l (see discussion in Hallward-Driemeier et al., 2002).
We use several estimates of TFP to check for robustness and minimize possible
biases. The simplest measure is obtained from a pooled OLS regression, in which all 3
main sectors (manufacturing, construction and services) are pooled together. This
8
means that all sectors have the same coefficients on capital and labor shares, however
they are allowed to have different intercepts (by means of industry dummies). We
refer to this measure as TFP_POOL. In addition, we run separate TFP regressions for
all 3 sectors, thus allowing each sector to have their sector-specific capital and labor
shares. The estimates obtained, TFP_S, allow production technology to differ across
main sectors.
Table 3 reports production function estimates obtained using pooled OLS across
sectors (TFP_POOL) and OLS by sector (TFP_S), which we find to be highly
correlated (correlation of 0.99). In the pooled regression, the labor and capital shares
are estimated to be around 0.3 and 0.8, which seems in line with conventional
wisdom. The regression is estimated with precision, with a surprisingly high R2 of
about 0.8. Interestingly, the manufacturing and services estimates are not significantly
different from each other, while construction appears to be more capital-intensive. We
also report value added TFP (TFP_VA), which we employ further on to perform
robustness checks on the estimation of the impact of the credit variable (column 5).6
In order to correct for the possible simultaneity bias of OLS estimation, we use two
approaches. First, we use instrumental variables, which appears suitable to the
structure of our dataset and the information it contains. Instruments that are correlated
with the input choice but not directly with productivity are likely to perform well.
Input prices are commonly used as instruments in TFP regressions (see for example,
Levinsohn and Petrin (2000)). The amount spent on wages is a natural choice to
instrument for (log) employment. Moreover, sampled firms report the average
6We do not use value-added estimates as our baseline because the number of observations is
significantly reduced when using this measure.
9
amount of annual sales they spent since 2001 for new buildings, machinery and
equipment, as well as for research and development (R&D). These variables were
mainly determined in the past ­ and as such are unlikely to be correlated with current
productivity shocks ­ but should predict well the current level of capital. We expect
past investment to be positively related to current capital stock. Conversely, R&D
expenditure is likely to be negatively related to capital, as firms that spend more on
R&D, which is intangible capital, usually spend less on tangible assets such as
machinery and equipment. The estimates of the first stage regressions reported in
columns (6) and (7) are statistically significant and of the expected sign. The first
stage regressions are estimated quite precisely ­ the labor equation has a R2 of 0.85
and the capital equation has an R2 of 0.6. The instrumental variable estimation of the
production function is reported in column (8). The labor and capital shares are not
significantly different from the OLS estimates, however the labor share is slightly
higher and the capital share is slightly lower than those obtained with OLS. The test
of over-identifying restrictions produces a p-value of 0.88, so we cannot reject the
hypothesis that our instruments are not correlated with the error (i.e. with productivity
shocks) in the main regression.
Our second approach to address simultaneity bias resulting from OLS estimation of
TFP is to adjust our TFP estimate to reflect possible biases. Levinsohn and Petrin
(2000) report the distribution of the difference between the OLS estimates and
unbiased estimates of labor and capital shares. OLS estimates on capital share are
usually biased downward (by about 0.05, on average) and labor shares are usually
biased upward (by about 0.05, on average). We use these estimates of biases to
10
correct TFP obtained from OLS regression and employ this additional variable
(TFP_ADJ) for further robustness checks.
Given the high correlation among the different estimates of TFP, we present our main
results using one of the estimates (TFP_S) and use the others to perform robustness
checks. We should also note that some recent research has highlighted that OLS and
2SLS TFP estimates do not differ substantially (see Eslava et al, 2004).
4. Access to credit and productivity
We first present OLS estimates of main correlates of firm TFP and assess the impact,
if any, of access to credit as proxied by the presence of a credit line or overdraft
facility. We then discuss the econometric problems associated with OLS estimation in
this context, discuss the validity of a set of instruments, and present 2SLS estimation.
Finally, we discuss a number of sensitivity checks to assess the robustness of our
estimation.
4.1 OLS estimates
We regress estimated TFP on a number of basic correlates and then add to this
baseline specification our main variable of inference. The results are reported in Table
4. We find that large enterprises are overall more productive, particularly if compared
to micro enterprises. As expected, companies that were previously government owned
are overall less productive. Also, younger firms are on average more productive.
11
However, neither effect is statistically significant, most likely because both variables
are highly correlated and pick up similar effects (we obtain more significant results
when only one of the two variables is included). Foreign ownership (as captured by a
dummy taking a value of 1 when more than 10% of the company is foreign owned)
does not seem to significantly affect productivity. Surprisingly, productivity is higher
in firms that sell most of their goods domestically.7
More importantly, having access to a credit line or an overdraft facility is positively
and significantly associated with higher productivity (model 3). The OLS estimate
suggests that going from not having access to having access is associated with an
increase in productivity by about 2/3 of a standard deviation.
It is important to note that credit line could proxy for a number of other firm
characteristics. In particular, the ability of managers (and workforce in general) might
be positively correlated with both access to credit and productivity. To control for
this possible source of bais, we use a measure of overall workforce education to proxy
for ability of managers. We find that while this proxy is significantly and positively
related to TFP, our access indicator is not affected by its inclusion . The overall
predicted power of our regression is improved with the inclusion of workforce
education, as the R2 increases from 0.16 to 0.25.
7Note that productivity appears to be higher in those firms that diversify their markets and sell both
domestically and abroad. In fact when a variable Export10, taking value of 1 when firms that export
more than 10% of their output is added to the current specification, both the Export10 dummy and
SELLDOM are significant and positive. The OLS and 2SLS estimates of the CREDIT coefficient are
robust to including or excluding the export dummy.
12
The positive association between access to credit and productivity can indicate either
that credit fosters productivity or that credit goes to more productive firms. Because
of this, OLS coefficients are likely to be biased. In the next section we will discuss
instrumental variable estimation to overcome this problem.
4.2 Instruments and 2SLS
Although the information in the survey is mainly cross sectional, firms were asked to
report whether their sales grew in 2001 and 2002. A priori we expect banks to be
more likely to extend credit to successful firms, and therefore to find a positive
correlation between LINE and indicators of positive growth in the past. Table 5
reports the first-stage regressions to predict LINE using dummies for recording a
positive growth in addition to other exogenous variables. We find indeed that growth
dummies are significant individually and jointly (F-stat of 5.27). At the same time, we
do not expect past growth to affect current productivity by channels other than the
access to credit. We subject this assumption to a test of over-identifying restrictions
and other robustness checks discussed below.
Model (1) in Table 6 reports the instrumental variables regression. We find LINE to
be significantly and positively associated with productivity. The test of over-
identifying restrictions cannot reject that the instruments are valid (P-value of 0.77).
13
4.3 Robustness checks
In this section we discuss a number of robustness checks that try to address legitimate
concerns with the IV strategy. First, we include other potentially important regressors
to rule out the possibility that the coefficient on LINE might capture the effect of
omitted variables. One might argue that foreign firms possess both higher know how
and thus are more productive and, at the same time, have a privileged access to credit.
However, when we include the foreign ownership dummy, the coefficient on LINE is
unchanged. Similarly, publicly owned firms might be both less productive and
systematically favored by banks. Nonetheless, including in the regression the percent
of capital that is state owned leaves our conclusions unchanged. One might also argue
that systematically paying bribes to obtain public services and licenses might both
lower firms productivity and also affect the extent to which firms can get access to
credit. To control for this potential issue, we include the percentage of sales that firms
report to be paying as bribes to public officials. Bribing is not significantly associated
with productivity nor is the coefficient on LINE affected by the inclusion of this
additional regressor. Finally, as in the OLS regression, we control for workforce
education, as a proxy for managerial ability, which in turn may influence productivity
and access to credit. We find, once again, that the estiamted coefficient on LINE is
not significantly affected.
Next, we assess whether our results are robust to using alternative measures of access
to credit. Specifically, we use the individual indicators for availability of credit line
only (CREDLINE) or availability of overdraft facility (OVERDRAFT). These
14
indicators are significant at 10% and 5% level respectively. This suggest that both
facilities are important for improving access.
We also assess whether our results are robust to using alternative measures of
productivity (Table 7). We find our results to be robust to using various productivity
estimates: column (1) reports TFP obtained from IV estimation; column (2) reports
estimates obtained by using value added instead of total sales as dependent variable in
production function; and column (3) reports TFP adjusted for possible biases
TFP_ADJ (discussed in the section 3).
Finally, instead of TFP we use two alternative measures of productivity ­ (log) sales
per employee and (log) sales per fixed capital. We find that LINE has the predicted
and significant effect on sales to employees ratio. However, the impact of credit on
sales per fixed capital, although of the expected sign, is not statistically significant.
Most likely, this result reflects the fact that sales per capital is reversely related to the
capital intensity of the firm, which has often been associated with increased access to
credit.
5. Conclusions
Although a vast literature highlights the positive impact of financial development on
growth, the evidence on the channels through which credit affects growth on the
micro level is still limited. We estimate whether access to credit has an impact on firm
productivity. To do so we first estimate TFP in a cross section of Bulgarian firms and
then assess the impact of access to credit on TFP. To overcome the potential omitted
15
variable bias problems with OLS estimates, we use information on past firm growth to
instrument for access to credit. When doing so we find credit to be strongly and
positively associated with productivity across firms. This result is robust to a number
of robustness checks, including using alternative estimates of TFP and a large set of
controls in the specification.
16
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18
Table 1. Tabulations
Panel A. Firm size and sector
In each cell, the first number is the number of firms, second number is row percent and third number is
column percent.
Size Manufacturing Construction Services Total
Micro 76 6 63 145
52.41 4.14 43.45 100
22.62 13.95 40.13 26.8
Small 113 13 52 181
62.43 7.18 28.73 100
33.63 30.23 33.12 33.46
Medium 67 12 24 103
65.05 11.65 23.3 100
19.94 27.91 15.29 19.04
Large 80 12 18 112
71.43 10.71 16.07 100
23.81 27.91 11.46 20.7
Total 336 43 157 541
62.11 7.95 29.02 100
100 100 100 100
Panel B. Firm size and access to credit
In each cell, the first number is the number of firms and the second number is the row percent.
Size Credit line or overdraft?
No Yes Total
Micro 125 14 139
89.93 10.07 100
Small 133 42 175
76 24 100
Medium 63 31 94
67.02 32.98 100
Large 47 58 105
44.76 55.24 100
Total 368 145 513
71.73 28.27 100
19
Table 2. Summary statistics
25th 50th 75th
Variable min percentile percentile percentile max mean sd N
Log Sales 0.00 4.88 6.52 7.82 11.55 6.36 1.99 271
Log Capital 0.00 4.11 5.99 7.24 11.03 5.70 2.29 205
Log Employment 0.69 2.40 3.43 4.54 7.38 3.49 1.34 541
Log Wages 0.69 3.22 4.50 5.56 9.02 4.44 1.61 353
Log sales / employees -0.22 2.15 2.78 3.34 8.96 2.78 1.10 270
Log Sales/ capital -3.67 -0.05 0.69 1.61 4.49 0.72 1.37 195
Investment 0.00 0.00 7.00 30.00 101.00 23.30 34.68 496
R&D 0.00 0.00 0.00 0.00 70.00 2.11 7.08 463
TFP -2.57 -0.60 -0.01 0.50 3.04 0.00 0.91 196
Log Age 0.00 2.08 2.56 3.26 4.82 2.68 0.86 548
Sales sold domestically (%) 0.00 70.00 100.00 100.00 100.00 80.37 33.27 534
Previous government
ownership dummy 0.00 0.00 0.00 1.00 1.00 0.39 0.49 546
Foreign ownership dummy 0.00 0.00 0.00 0.00 1.00 0.10 0.29 545
Workforce education 0.00 7.00 20.00 33.00 100.00 25.45 27.11 525
Positive growth in 2001 0.00 0.00 1.00 1.00 1.00 0.52 0.50 338
Positive growth in 2002 0.00 0.00 1.00 1.00 1.00 0.52 0.50 347
20
Table 3. Estimating TFP
Capital is measured by fixed assets, employment is measured by number of people; Investment is new buildings, machinery and equipment, expressed
as % of the annual sales, R&D is Research and development, expressed as % of the annual sales. Model (8) is estimated by IV (using log wage,
investment and R&D as instruments), while models (1)-(7) are estimated by OLS. Robust t statistics in brackets, *, ** and *** indicate significance at
10%, 5% and 1% respectively. "Overid p-value" is a p-value for overidentification test.
(1) (2) (3) (4) (5) (6) (7) (8)
Dependent variable Log Sales Log Sales Log Sales Log Sales Value Log Log Capital Log Sales
added Employment
Sectors: all sectors manufacturing construction services all sectors all sectors all sectors IV all
sectors
Log capital 0.302 0.271 0.376 0.332 0.404 0.250
[6.23]*** [4.07]*** [1.98]* [4.24]*** [6.04]*** [1.02]
Log employment 0.793 0.822 0.675 0.791 0.533 0.930
[10.24]*** [8.18]*** [1.22] [6.02]*** [5.35]*** [2.85]***
Manufacturing dummy 0.006 -0.400 0.188 0.087 -0.004
[0.04] [1.88]* [2.65]*** [0.30] [0.02]
Construction dummy 0.297 0.078 0.249 -0.198 0.096
[1.29] [0.27] [2.41]** [0.42] [0.34]
Log Wage 0.774 1.033
[38.92]*** [13.94]***
Investment 0.001 0.014
[1.38] [2.48]**
R&D -0.005 -0.022
[2.10]** [1.92]*
Constant 1.727 1.808 2.092 1.582 6.305 -0.064 0.799 1.535
[9.03]*** [8.22]*** [1.42] [5.90]*** [22.92]*** [0.64] [2.05]** [4.41]***
Observations 196 131 20 45 130 313 169 165
R-squared 0.78 0.78 0.61 0.78 0.73 0.85 0.60
Overid p-value: 0.88
21
Table 4. Access and productivity, OLS
LINE is a dummy variable for firms that have either credit line or an overdraft facility. Workforce
education is measured by percent workforce with over 12 years education (university and post-graduate).
Robust t statistics in brackets, *, ** and *** indicate significance at 10%, 5% and 1% respectively.
(1) (2) (3) (4)
Dependent variable: TFP TFP TFP TFP
Micro firm dummy -0.676 -0.644 -0.360 -0.393
[3.12]*** [2.92]*** [1.72]* [2.00]**
Small firm dummy -0.259 -0.224 -0.039 -0.136
[1.33] [1.17] [0.19] [0.71]
Medium firm dummy -0.484 -0.490 -0.338 -0.425
[2.74]*** [2.73]*** [1.97]* [2.69]***
Previous government ownership -0.277 -0.298 -0.171 -0.161
[1.40] [1.53] [0.85] [0.83]
Log age -0.092 -0.080 -0.122 -0.084
[0.86] [0.77] [1.21] [0.88]
Sales sold domestically (%) 0.005 0.006 0.006 0.006
[2.45]** [2.73]*** [3.09]*** [3.11]***
Manufacturing dummy 0.075 0.079 0.101 0.125
[0.44] [0.46] [0.61] [0.76]
Construction dummy -0.103 -0.076 -0.057 0.041
[0.44] [0.32] [0.25] [0.19]
Foreign ownership 0.251
[1.03]
Line 0.561 0.489
[3.57]*** [3.41]***
Workforce education 0.012
[4.40]***
Constant 0.335 0.203 -0.131 -0.368
[1.03] [0.60] [0.40] [1.13]
Observations 192 190 191 187
R-squared 0.09 0.11 0.16 0.25
22
Table 5. First stage ­ access to credit
Dependent variable is LINE. Robust t statistics in brackets, *, ** and *** indicate significance at
10%, 5% and 1% respectively.
(1) (2) (3)
Dependent variable: Line Line Line
Micro firm dummy -0.458 -0.461 -0.444
[5.44]*** [5.50]*** [5.23]***
Small firm dummy -0.352 -0.361 -0.347
[4.34]*** [4.52]*** [4.28]***
Medium firm dummy -0.235 -0.260 -0.240
[2.78]*** [3.12]*** [2.84]***
Previous government -0.028 -0.035 -0.030
ownership
[0.39] [0.50] [0.41]
Log age 0.011 0.020 0.017
[0.24] [0.45] [0.37]
Sales sold domestically (%) -0.001 -0.001 -0.001
[0.94] [0.77] [0.92]
Manufacturing dummy 0.010 0.000 -0.001
[0.13] [0.01] [0.01]
Construction dummy 0.197 0.213 0.238
[1.41] [1.55] [1.61]
Positive growth in 2001 0.138 0.076
[2.90]*** [1.25]
Positive growth in 2002 0.147 0.093
[3.22]*** [1.58]
Constant 0.490 0.468 0.462
[3.15]*** [3.06]*** [2.97]***
Observations 312 321 311
R-squared 0.22 0.21 0.22
23
Table 6. Access and productivity, Instrumental Variables.
LINE is a dummy variable for firms with credit line or overdraft; Informal payments are payments to public officials (% of
sales); Workforce education is measured by percent workforce with over 12 years education (university and post-graduate).
"Overid p-value" is a p-value for overidentification test. Robust t statistics in brackets, *, ** and *** indicate significance at
10%, 5% and 1% respectively.
(1) (2) (3) (4) (5) (6) (7)
Dependent variable: TFP TFP TFP TFP TFP TFP TFP
Line 2.941 3.137 2.860 2.932 2.629
[2.35]** [2.33]** [2.14]** [1.83]* [2.17]**
Micro firm dummy 1.223 1.412 1.162 1.093 1.095 1.511 0.523
[1.51] [1.60] [1.34] [1.02] [1.37] [1.26] [0.96]
Small firm dummy 1.084 1.238 1.031 1.176 0.976 1.191 0.539
[1.56] [1.63] [1.39] [1.25] [1.46] [1.33] [1.11]
Medium firm dummy 0.492 0.575 0.467 0.361 0.387 0.703 -0.339
[0.98] [1.07] [0.91] [0.62] [0.78] [0.95] [1.14]
Previous government ownership 0.443 0.493 0.433 0.480 0.462 0.664 0.523
[0.97] [1.02] [0.96] [0.77] [1.04] [1.12] [1.19]
Log Age -0.343 -0.341 -0.328 -0.416 -0.328 -0.446 -0.413
[1.55] [1.45] [1.43] [1.45] [1.45] [1.47] [2.18]**
Sales sold domestically (%) 0.010 0.011 0.010 0.009 0.008 0.014 0.002
[2.45]** [2.41]** [2.51]** [2.07]** [2.13]** [2.03]** [0.55]
Manufacturing dummy -0.633 -0.640 -0.603 -0.633 -0.577 -0.469 -0.684
[1.17] [1.10] [1.08] [1.03] [1.13] [0.70] [1.61]
Construction dummy -0.910 -0.933 -0.895 -0.615 -0.714 -0.660 -0.246
[1.18] [1.13] [1.16] [0.75] [0.98] [0.68] [0.49]
Foreign owned 0.387
[1.16]
% State owned -0.004
[0.41]
Informal payments 0.007
[0.30]
Workforce education 0.008
[1.54]
Credit line 4.192
[1.83]*
Overdraft 2.677
[2.34]**
Constant -1.056 -1.345 -1.047 -0.798 -0.987 -1.622 0.635
[1.25]** [1.50] [1.26] [0.76] [1.35] [1.21] [1.39]
Observations 134 134 134 103 130 133 130
Overid p-value: 0.77 0.83 0.78 0.69 0.78 0.78 0.84
24
Table 7. Robustness checks on TFP definition.
Robust t statistics in brackets, *, ** and *** indicate significance at 10%, 5% and 1% respectively.
(1) (2) (3) (4) (5)
Dependent variable: TFP /IV TFP_VA TFP_ADJ Log sales/ Log Sales/
employees capital
Line 2.991 3.529 2.823 4.162 1.293
[2.37]** [2.35]** [2.30]** [2.04]** [0.97]
Micro firm dummy 1.528 2.234 1.151 1.832 -0.146
[1.87]* [2.16]** [1.46] [1.46] [0.19]
Small firm dummy 1.254 1.561 1.047 0.919 0.386
[1.78]* [1.82]* [1.54] [0.97] [0.58]
Medium firm dummy 0.574 0.967 0.464 0.801 0.003
[1.13] [1.60] [0.96] [1.16] [0.01]
Previously government owned 0.465 0.280 0.388 0.236 -0.327
[1.00] [0.48] [0.87] [0.41] [0.71]
Log age -0.356 -0.138 -0.350 0.021 -0.447
[1.57] [0.46] [1.62] [0.08] [2.12]**
Sales sold domestically (%) 0.011 0.012 0.010 0.011 0.006
[2.55]** [1.69]* [2.42]** [1.91]* [1.30]
Manufacturing dummy -0.618 -1.000 -0.623 -0.621 -0.537
[1.14] [1.47] [1.16] [1.18] [0.85]
Construction dummy -0.699 -1.423 -0.873 -0.871 -0.133
[0.90] [1.29] [1.14] [0.91] [0.16]
Constant -1.246 1.749 -1.022 0.150 1.791
[1.45] [1.20] [1.24] [0.11] [2.02]**
Observations 134 98 134 169 133
Overid p-value: 0.74 0.85 0.83 0.59 0.44
25